Overview

Dataset statistics

Number of variables8
Number of observations5863014
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory357.9 MiB
Average record size in memory64.0 B

Variable types

NUM8

Warnings

p_t_vis_1 has unique values Unique
p_t_vis_2 has unique values Unique

Reproduction

Analysis started2020-11-30 07:43:55.269189
Analysis finished2020-11-30 07:50:15.888358
Duration6 minutes and 20.62 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

sv_x_1
Real number (ℝ)

Distinct5701019
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0002446417092
Minimum-19.95230865
Maximum22.21428871
Zeros12
Zeros (%)< 0.1%
Memory size44.7 MiB
2020-11-30T15:50:19.029356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-19.95230865
5-th percentile-0.5788607717
Q1-0.1201764792
median3.731343895e-06
Q30.1200574189
95-th percentile0.5806857497
Maximum22.21428871
Range42.16659737
Interquartile range (IQR)0.2402338982

Descriptive statistics

Standard deviation0.4179645129
Coefficient of variation (CV)1708.476099
Kurtosis37.91111022
Mean0.0002446417092
Median Absolute Deviation (MAD)0.1201185174
Skewness0.1182086152
Sum1434.337766
Variance0.174694334
MonotocityNot monotonic
2020-11-30T15:50:19.189354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
012< 0.1%
 
0.3311889474< 0.1%
 
-0.046356707814< 0.1%
 
0.017797932034< 0.1%
 
0.25144144894< 0.1%
 
0.14854696394< 0.1%
 
-0.26047265534< 0.1%
 
-0.42171901464< 0.1%
 
-0.064703211194< 0.1%
 
-0.1427941624< 0.1%
 
0.29388606554< 0.1%
 
-0.041026927534< 0.1%
 
0.19355192784< 0.1%
 
-0.079528108244< 0.1%
 
-0.45727425814< 0.1%
 
-0.088641770184< 0.1%
 
0.28593528274< 0.1%
 
0.085466988384< 0.1%
 
-1.2027559284< 0.1%
 
-0.059112492954< 0.1%
 
0.27777451284< 0.1%
 
0.073690928524< 0.1%
 
-0.0088648349054< 0.1%
 
0.17941668634< 0.1%
 
-0.34256127484< 0.1%
 
Other values (5700994)5862906> 99.9%
 
ValueCountFrequency (%) 
-19.952308651< 0.1%
 
-16.897489551< 0.1%
 
-15.892579081< 0.1%
 
-15.568434721< 0.1%
 
-15.153254511< 0.1%
 
-15.007181171< 0.1%
 
-14.141970631< 0.1%
 
-14.007982251< 0.1%
 
-13.874873161< 0.1%
 
-13.143564221< 0.1%
 
ValueCountFrequency (%) 
22.214288711< 0.1%
 
18.402732851< 0.1%
 
17.796752931< 0.1%
 
16.822587971< 0.1%
 
16.547773361< 0.1%
 
15.010859491< 0.1%
 
14.877805711< 0.1%
 
13.909631731< 0.1%
 
12.604305271< 0.1%
 
12.198864941< 0.1%
 

sv_y_1
Real number (ℝ)

Distinct5647327
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0002027701762
Minimum-23.09705544
Maximum19.20538712
Zeros12
Zeros (%)< 0.1%
Memory size44.7 MiB
2020-11-30T15:50:22.311338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-23.09705544
5-th percentile-0.5787646413
Q1-0.1198664308
median-5.943700671e-06
Q30.1199434586
95-th percentile0.5804248899
Maximum19.20538712
Range42.30244255
Interquartile range (IQR)0.2398098893

Descriptive statistics

Standard deviation0.4177841625
Coefficient of variation (CV)2060.382697
Kurtosis36.27257786
Mean0.0002027701762
Median Absolute Deviation (MAD)0.1199057754
Skewness0.08525363388
Sum1188.844382
Variance0.1745436064
MonotocityNot monotonic
2020-11-30T15:50:22.475336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
012< 0.1%
 
-4.981458187e-056< 0.1%
 
-0.00068473815925< 0.1%
 
-0.0030880272395< 0.1%
 
-0.00082798302175< 0.1%
 
-0.00080963969235< 0.1%
 
0.0012407749895< 0.1%
 
2.129375935e-055< 0.1%
 
8.73208046e-055< 0.1%
 
-0.00015738606455< 0.1%
 
-0.065308183435< 0.1%
 
-9.603798389e-055< 0.1%
 
9.496510029e-055< 0.1%
 
0.0032674521215< 0.1%
 
0.0011907666925< 0.1%
 
-0.0004389137035< 0.1%
 
-6.555020809e-055< 0.1%
 
-0.0063907057054< 0.1%
 
-0.0016046017414< 0.1%
 
0.13556841024< 0.1%
 
0.0020712614064< 0.1%
 
0.15644019844< 0.1%
 
-0.0068635046484< 0.1%
 
0.066778019074< 0.1%
 
-0.00021408498294< 0.1%
 
Other values (5647302)5862889> 99.9%
 
ValueCountFrequency (%) 
-23.097055441< 0.1%
 
-13.969072341< 0.1%
 
-13.061469081< 0.1%
 
-13.035711291< 0.1%
 
-12.88735391< 0.1%
 
-12.156787871< 0.1%
 
-12.03122521< 0.1%
 
-11.890095711< 0.1%
 
-11.709756851< 0.1%
 
-11.587900161< 0.1%
 
ValueCountFrequency (%) 
19.205387121< 0.1%
 
18.816587451< 0.1%
 
17.0901681< 0.1%
 
17.063333511< 0.1%
 
16.167417531< 0.1%
 
15.386545181< 0.1%
 
13.324284551< 0.1%
 
13.145638471< 0.1%
 
12.699006081< 0.1%
 
12.568383221< 0.1%
 

sv_z_1
Real number (ℝ)

Distinct4439079
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0003523229496
Minimum-75.28164673
Maximum46.86742401
Zeros41
Zeros (%)< 0.1%
Memory size44.7 MiB
2020-11-30T15:50:24.881350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-75.28164673
5-th percentile-1.339442307
Q1-0.2003314793
median-1.859664917e-05
Q30.199906826
95-th percentile1.336519092
Maximum46.86742401
Range122.1490707
Interquartile range (IQR)0.4002383053

Descriptive statistics

Standard deviation1.069870718
Coefficient of variation (CV)-3036.61944
Kurtosis52.27733106
Mean-0.0003523229496
Median Absolute Deviation (MAD)0.200122498
Skewness-0.06590174981
Sum-2065.674386
Variance1.144623353
MonotocityNot monotonic
2020-11-30T15:50:25.056333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
041< 0.1%
 
2.861022949e-0624< 0.1%
 
-1.14440918e-0524< 0.1%
 
-1.430511475e-0623< 0.1%
 
5.626678467e-0523< 0.1%
 
-0.000387191772521< 0.1%
 
-2.813339233e-0521< 0.1%
 
-0.000548362731921< 0.1%
 
-0.00020503997821< 0.1%
 
-5.722045898e-0621< 0.1%
 
-9.632110596e-0520< 0.1%
 
-6.675720215e-0620< 0.1%
 
-8.106231689e-0620< 0.1%
 
8.106231689e-0620< 0.1%
 
4.768371582e-0620< 0.1%
 
-1.71661377e-0520< 0.1%
 
-5.81741333e-0520< 0.1%
 
1.907348633e-0620< 0.1%
 
-1.049041748e-0520< 0.1%
 
2.574920654e-0520< 0.1%
 
-0.000185966491720< 0.1%
 
4.768371582e-0719< 0.1%
 
-6.38961792e-0519< 0.1%
 
-0.000133514404319< 0.1%
 
-0.000291824340819< 0.1%
 
Other values (4439054)5862478> 99.9%
 
ValueCountFrequency (%) 
-75.281646731< 0.1%
 
-54.593456271< 0.1%
 
-50.193794251< 0.1%
 
-47.049163821< 0.1%
 
-39.884704591< 0.1%
 
-37.464290621< 0.1%
 
-34.189605711< 0.1%
 
-33.543918611< 0.1%
 
-33.392826081< 0.1%
 
-33.333305361< 0.1%
 
ValueCountFrequency (%) 
46.867424011< 0.1%
 
40.839523321< 0.1%
 
39.68124391< 0.1%
 
38.193946841< 0.1%
 
37.083683011< 0.1%
 
36.528049471< 0.1%
 
35.99553681< 0.1%
 
35.199432371< 0.1%
 
35.01526261< 0.1%
 
34.178718571< 0.1%
 

sv_x_2
Real number (ℝ)

Distinct5701779
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.032575049e-05
Minimum-27.17522049
Maximum22.15626717
Zeros2
Zeros (%)< 0.1%
Memory size44.7 MiB
2020-11-30T15:50:28.212426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-27.17522049
5-th percentile-0.5817135245
Q1-0.1188049316
median-8.115544915e-06
Q30.1190067958
95-th percentile0.5813378572
Maximum22.15626717
Range49.33148766
Interquartile range (IQR)0.2378117274

Descriptive statistics

Standard deviation0.4202702168
Coefficient of variation (CV)5232.07333
Kurtosis42.76985174
Mean8.032575049e-05
Median Absolute Deviation (MAD)0.1189099699
Skewness-0.04238955891
Sum470.9509997
Variance0.1766270552
MonotocityNot monotonic
2020-11-30T15:50:28.373427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.030517756945< 0.1%
 
-0.20840471984< 0.1%
 
-0.032660961154< 0.1%
 
0.0021741986274< 0.1%
 
0.12935058774< 0.1%
 
-0.14678329234< 0.1%
 
-0.218513314< 0.1%
 
-0.00013929605484< 0.1%
 
-0.00023938715464< 0.1%
 
-0.30830225354< 0.1%
 
-0.00021661072974< 0.1%
 
-0.021744363014< 0.1%
 
0.10838307444< 0.1%
 
0.58516126874< 0.1%
 
-0.12591427564< 0.1%
 
0.21809343994< 0.1%
 
0.24430972344< 0.1%
 
0.1403276924< 0.1%
 
0.047951988884< 0.1%
 
-0.07217584554< 0.1%
 
-0.079357042914< 0.1%
 
0.13894394044< 0.1%
 
-0.19775071744< 0.1%
 
1.822412014e-054< 0.1%
 
-0.14863175154< 0.1%
 
Other values (5701754)5862913> 99.9%
 
ValueCountFrequency (%) 
-27.175220491< 0.1%
 
-17.26001741< 0.1%
 
-17.12279511< 0.1%
 
-16.733791351< 0.1%
 
-16.70867921< 0.1%
 
-16.398611071< 0.1%
 
-16.092449191< 0.1%
 
-15.759416581< 0.1%
 
-15.208720211< 0.1%
 
-14.214802741< 0.1%
 
ValueCountFrequency (%) 
22.156267171< 0.1%
 
19.724552151< 0.1%
 
19.477188111< 0.1%
 
18.107568741< 0.1%
 
15.55899621< 0.1%
 
14.776826861< 0.1%
 
14.131481171< 0.1%
 
14.102197651< 0.1%
 
13.989938741< 0.1%
 
12.967567441< 0.1%
 

sv_y_2
Real number (ℝ)

Distinct5645816
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.297711617e-05
Minimum-24.42358208
Maximum18.51883698
Zeros4
Zeros (%)< 0.1%
Memory size44.7 MiB
2020-11-30T15:50:31.532428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-24.42358208
5-th percentile-0.5827077448
Q1-0.1188236214
median4.302710295e-06
Q30.1191416215
95-th percentile0.5816628546
Maximum18.51883698
Range42.94241905
Interquartile range (IQR)0.2379652429

Descriptive statistics

Standard deviation0.4195247442
Coefficient of variation (CV)7918.980392
Kurtosis36.86754728
Mean5.297711617e-05
Median Absolute Deviation (MAD)0.1189795993
Skewness0.07848117704
Sum310.6055738
Variance0.176001011
MonotocityNot monotonic
2020-11-30T15:50:31.688427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.023827090865< 0.1%
 
-0.00042942166335< 0.1%
 
-0.0014409869915< 0.1%
 
0.0046892464165< 0.1%
 
-1.79335475e-055< 0.1%
 
0.00016169250015< 0.1%
 
0.0099518746145< 0.1%
 
0.0020680576565< 0.1%
 
-0.0015217512855< 0.1%
 
-0.0018649846325< 0.1%
 
9.882450104e-055< 0.1%
 
0.001958638435< 0.1%
 
-0.0019766986375< 0.1%
 
0.0014116913085< 0.1%
 
-0.0017845928675< 0.1%
 
0.00149884825< 0.1%
 
0.009962216025< 0.1%
 
0.10199637714< 0.1%
 
-0.19078689814< 0.1%
 
-0.0029849112034< 0.1%
 
0.022059500224< 0.1%
 
-0.0018120408064< 0.1%
 
-0.0023308545354< 0.1%
 
-0.0051274895674< 0.1%
 
-0.16784724594< 0.1%
 
Other values (5645791)5862897> 99.9%
 
ValueCountFrequency (%) 
-24.423582081< 0.1%
 
-16.877008441< 0.1%
 
-14.259493831< 0.1%
 
-13.414292341< 0.1%
 
-13.25025941< 0.1%
 
-12.971973421< 0.1%
 
-12.638147351< 0.1%
 
-11.940178871< 0.1%
 
-11.540247921< 0.1%
 
-11.352211951< 0.1%
 
ValueCountFrequency (%) 
18.518836981< 0.1%
 
16.419942861< 0.1%
 
15.257247921< 0.1%
 
15.191569331< 0.1%
 
14.854864121< 0.1%
 
14.2167121< 0.1%
 
14.021134381< 0.1%
 
13.31272031< 0.1%
 
13.037688261< 0.1%
 
12.84791471< 0.1%
 

sv_z_2
Real number (ℝ)

Distinct4465449
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.000233997507
Minimum-132.0452728
Maximum134.4137115
Zeros25
Zeros (%)< 0.1%
Memory size44.7 MiB
2020-11-30T15:50:34.083427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-132.0452728
5-th percentile-1.387030482
Q1-0.2061831653
median-1.430511475e-05
Q30.2054956332
95-th percentile1.385781324
Maximum134.4137115
Range266.4589844
Interquartile range (IQR)0.4116787985

Descriptive statistics

Standard deviation1.236014105
Coefficient of variation (CV)-5282.167835
Kurtosis361.4626708
Mean-0.000233997507
Median Absolute Deviation (MAD)0.2058486938
Skewness0.5402940788
Sum-1371.93066
Variance1.527730868
MonotocityNot monotonic
2020-11-30T15:50:34.242428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
025< 0.1%
 
-2.861022949e-0624< 0.1%
 
-9.536743164e-0723< 0.1%
 
-4.768371582e-0722< 0.1%
 
4.577636719e-0522< 0.1%
 
-1.621246338e-0522< 0.1%
 
-2.479553223e-0521< 0.1%
 
-0.000107765197821< 0.1%
 
9.536743164e-0721< 0.1%
 
8.964538574e-0521< 0.1%
 
-0.000185012817420< 0.1%
 
1.525878906e-0520< 0.1%
 
0.000293731689520< 0.1%
 
0.000198364257820< 0.1%
 
9.441375732e-0519< 0.1%
 
8.106231689e-0619< 0.1%
 
6.675720215e-0619< 0.1%
 
6.294250488e-0519< 0.1%
 
9.536743164e-0619< 0.1%
 
-1.430511475e-0519< 0.1%
 
-0.000133514404318< 0.1%
 
-3.242492676e-0518< 0.1%
 
-0.000206947326718< 0.1%
 
2.861022949e-0618< 0.1%
 
-0.000569343566918< 0.1%
 
Other values (4465424)5862508> 99.9%
 
ValueCountFrequency (%) 
-132.04527281< 0.1%
 
-99.502838131< 0.1%
 
-95.850189211< 0.1%
 
-93.487197881< 0.1%
 
-91.167137151< 0.1%
 
-88.225189211< 0.1%
 
-86.884872441< 0.1%
 
-85.626930241< 0.1%
 
-82.802307131< 0.1%
 
-80.237823491< 0.1%
 
ValueCountFrequency (%) 
134.41371151< 0.1%
 
131.56565861< 0.1%
 
116.23487851< 0.1%
 
109.2263871< 0.1%
 
106.62092591< 0.1%
 
102.10072331< 0.1%
 
100.74456021< 0.1%
 
95.817512511< 0.1%
 
89.773757931< 0.1%
 
83.449485781< 0.1%
 

p_t_vis_1
Real number (ℝ≥0)

UNIQUE

Distinct5863014
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.8019413
Minimum0.02586738243
Maximum2541.851816
Zeros0
Zeros (%)0.0%
Memory size44.7 MiB
2020-11-30T15:50:38.494427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.02586738243
5-th percentile36.64026692
Q152.1872514
median75.66707106
Q3123.6020144
95-th percentile245.8841433
Maximum2541.851816
Range2541.825949
Interquartile range (IQR)71.414763

Descriptive statistics

Standard deviation76.56432612
Coefficient of variation (CV)0.7595520995
Kurtosis18.86732979
Mean100.8019413
Median Absolute Deviation (MAD)28.96641922
Skewness3.006351939
Sum591003192.8
Variance5862.096034
MonotocityNot monotonic
2020-11-30T15:50:38.664427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
66.34401121< 0.1%
 
81.453080871< 0.1%
 
40.670407011< 0.1%
 
163.44113171< 0.1%
 
93.496512871< 0.1%
 
125.49299981< 0.1%
 
75.805007651< 0.1%
 
49.839679481< 0.1%
 
138.17262591< 0.1%
 
190.52647281< 0.1%
 
60.257510171< 0.1%
 
64.529635571< 0.1%
 
52.544428231< 0.1%
 
107.0618341< 0.1%
 
178.17645331< 0.1%
 
300.27903261< 0.1%
 
36.018268591< 0.1%
 
213.32025881< 0.1%
 
79.878419151< 0.1%
 
248.14494511< 0.1%
 
187.77270931< 0.1%
 
41.441950271< 0.1%
 
54.69589521< 0.1%
 
32.265823941< 0.1%
 
88.684733871< 0.1%
 
Other values (5862989)5862989> 99.9%
 
ValueCountFrequency (%) 
0.025867382431< 0.1%
 
0.10093787561< 0.1%
 
0.13062596941< 0.1%
 
0.14053837961< 0.1%
 
0.15286628311< 0.1%
 
0.16312032691< 0.1%
 
0.16684634621< 0.1%
 
0.1738938991< 0.1%
 
0.19410826391< 0.1%
 
0.21233586821< 0.1%
 
ValueCountFrequency (%) 
2541.8518161< 0.1%
 
2173.0232731< 0.1%
 
2173.0124241< 0.1%
 
2170.6824671< 0.1%
 
2103.1402841< 0.1%
 
2058.1075671< 0.1%
 
2055.1626291< 0.1%
 
1991.8994021< 0.1%
 
1958.1241281< 0.1%
 
1852.0470371< 0.1%
 

p_t_vis_2
Real number (ℝ≥0)

UNIQUE

Distinct5863014
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.4265086
Minimum0.01332170947
Maximum3734.552375
Zeros0
Zeros (%)0.0%
Memory size44.7 MiB
2020-11-30T15:50:42.920415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01332170947
5-th percentile36.70132823
Q152.48200575
median76.16637443
Q3124.7360844
95-th percentile248.0951704
Maximum3734.552375
Range3734.539054
Interquartile range (IQR)72.25407866

Descriptive statistics

Standard deviation78.11631194
Coefficient of variation (CV)0.7701764857
Kurtosis23.78496867
Mean101.4265086
Median Absolute Deviation (MAD)29.25413625
Skewness3.158468583
Sum594665039.8
Variance6102.158191
MonotocityNot monotonic
2020-11-30T15:50:43.088427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
73.224823971< 0.1%
 
59.835418221< 0.1%
 
133.37018891< 0.1%
 
255.15477791< 0.1%
 
112.14987181< 0.1%
 
7.6195128021< 0.1%
 
20.313008871< 0.1%
 
136.8144081< 0.1%
 
93.563107191< 0.1%
 
333.82725741< 0.1%
 
67.27341651< 0.1%
 
81.60455581< 0.1%
 
44.146657761< 0.1%
 
156.91742341< 0.1%
 
76.056371071< 0.1%
 
113.50366761< 0.1%
 
58.770146031< 0.1%
 
267.34476471< 0.1%
 
143.79323721< 0.1%
 
47.601387751< 0.1%
 
86.326439531< 0.1%
 
137.51818631< 0.1%
 
68.658731291< 0.1%
 
85.023716551< 0.1%
 
70.687280121< 0.1%
 
Other values (5862989)5862989> 99.9%
 
ValueCountFrequency (%) 
0.013321709471< 0.1%
 
0.04098931741< 0.1%
 
0.043534357071< 0.1%
 
0.046239221731< 0.1%
 
0.050925562261< 0.1%
 
0.055113365831< 0.1%
 
0.058462792781< 0.1%
 
0.064571733411< 0.1%
 
0.068708263091< 0.1%
 
0.071207671741< 0.1%
 
ValueCountFrequency (%) 
3734.5523751< 0.1%
 
3175.1700181< 0.1%
 
3145.6620631< 0.1%
 
2740.7671831< 0.1%
 
2652.0701941< 0.1%
 
2538.0385051< 0.1%
 
2486.9027281< 0.1%
 
2450.0507541< 0.1%
 
2421.6737121< 0.1%
 
2408.0712521< 0.1%
 

Interactions

2020-11-30T15:47:17.582256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:19.819645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:21.938623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:24.207640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:26.331625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:28.429645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:30.579645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:32.706626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:34.844627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:36.960625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:39.041640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:41.142645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:43.243644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:45.354640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:47.499626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:49.618629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:51.768645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:53.870645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:55.966624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:47:58.076639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:00.191647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:02.303668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:04.530653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:06.794668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:08.974651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:11.066670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:13.156668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:15.254652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:17.368647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:19.489668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:21.637647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:23.837651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:26.157647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:28.312647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:30.480650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:32.636668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:34.756668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:36.923663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:39.190648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:41.446670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:43.689651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:45.839663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:48.039650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:50.192650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:52.624652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:54.752648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:56.961663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:48:59.184653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:01.470663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:03.665667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:05.889752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:08.201649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:10.461647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:12.772647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:15.083668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:17.331651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:19.574668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:21.774646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:24.068649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:26.635651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:28.869648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:31.012647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:33.290648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:35.603647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-30T15:50:43.253411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-30T15:50:43.495887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-30T15:50:43.731887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-30T15:50:43.974892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-30T15:49:51.438663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T15:49:54.779663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

sv_x_1sv_y_1sv_z_1sv_x_2sv_y_2sv_z_2p_t_vis_1p_t_vis_2
00.0235830.0193930.005763-0.581665-0.5139090.01812633.75304152.583766
10.264949-0.061602-0.161944-0.120283-0.045656-0.26019637.600309127.553300
20.2388450.2339970.112015-0.2800710.0572880.05997159.09159067.625778
3-0.0054630.0553900.0929980.429833-1.0082763.83666165.426963145.224950
40.0669190.085073-0.0957570.0656690.0543460.02704656.05096480.801777
5-0.1201080.099172-0.1788770.113925-0.092769-0.43707783.218779128.657979
60.1980500.0598600.352347-0.093828-0.0662790.317293135.957500102.965963
70.102937-0.0835490.053707-0.2161660.1485080.08576343.15778050.276410
80.258645-0.114208-0.215292-0.0228940.022106-0.04171042.01175478.981413
90.234155-0.2815281.579283-0.3059160.3954071.754387222.673875158.471221

Last rows

sv_x_1sv_y_1sv_z_1sv_x_2sv_y_2sv_z_2p_t_vis_1p_t_vis_2
58630040.350872-0.0790261.186877-0.0092110.0318130.027618149.56874653.201092
58630050.126302-1.4162152.4941350.0615980.0011540.05059893.96679770.720492
58630060.237066-0.0775400.696777-0.0031760.0061550.003661195.89040343.742224
5863007-0.0118800.0436570.059585-0.065304-0.0835700.221236113.399669114.933981
5863008-0.206547-1.320174-0.840416-0.052679-0.027292-0.00571476.29787141.280920
58630090.0522360.0145280.0031330.5846500.334812-0.71192069.400115279.673862
58630100.0054420.009449-0.0002910.112904-0.3233240.00665697.10739937.961199
58630110.025354-0.0091830.0221120.0170880.004278-0.02821946.20581486.167163
5863012-0.0300710.2235680.407931-0.386126-0.1972051.09197580.176336169.524357
58630130.046878-0.206266-0.1207270.120289-0.073203-0.212698153.087912129.565796